Evolution-Informed Modeling and Control of Tumor Growth

Cancer development is an evolutionary process that mirrors the evolution of biological species. While species evolve through mutations and natural selection acting on individuals within a population, cancer evolves through mutations and selection acting on cells within a tissue. This process is further complicated by factors such as tumor cell heterogeneity, cellular interactions, and the selective pressure exerted by the tumor microenvironment. Understanding these mechanisms and the evolutionary dynamics of cancer cells is crucial to gaining deeper insights into the disease. This paper first presents a refined eco-evolutionary model of cancer, with key parameters grounded in experimental data, explicitly incorporating the aforementioned aspects. Building upon this, we propose a framework to design and compare evolutionarily informed therapeutic control strategies derived from multi-objective optimization. These strategies address the multifaceted nature of cancer treatment, seeking to simultaneously minimize tumor size and the development of therapeutic resistance, while maximizing the patient’s quality of life. Simulations validate the model and show the efficacy of these evolution-aware therapies in managing tumor progression while mitigating treatment-related side effects.